Tensor based framework for Distributed Denial of Service attack detection

Maranhão, João Paulo A.; Costa, João Paulo; Javidi, Elnaz; Andrade, César Augusto Borges de; Sousa, Rafael T. de

doi:10.1016/j.jnca.2020.102894

Cited by 18 publications

(6 citation statements)

References 28 publications

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“…Our method achieves a high detection rate of 90% in addition to zero false positive rate for ESynFlood trace, while it achieves a detection rate of 100% with zero false positive rate for both of CICDDoS2019 traces. The proposed method proved its efficiency as it outperforms the previous detection methods in [15], [16], [17], [18] whose detection rate ranged between 98% and 99.9% for CICDDoS2019 dataset although depending on training step Table. II shows that comparison.…”

Section: Odds Ratio Valuesmentioning

confidence: 84%

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Detection of Distributed Denial of Service Flooding Attack Using Odds Ratio

Nashat¹,

Hussain²,

Jiang³

2021

J-NaNA

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Computer networks are vulnerable to many types of attacks while the Distributed Denial of Service attack (DDoS) serves as one of the top concerns for security professionals. The DDoS flooding attack denies the services by consuming the server resources to prevent the legitimate users from using their desired services. The hardness of detecting this attack lies in sending a stream of packets to the server with spoofed IP addresses, so that the internet routing infrastructure cannot distinguish the spoofed packets. Based on the odds ratio (OR) statistical measurement, in this work we propose a new detection method for the DDoS flooding attacks. By exploring the odds ratio to determine the risk factor of any incoming traffic to the server, the legitimate and attack traffic packets can be easily differentiated. Experimental results demonstrate the efficiency of the presented detection method in terms of its detection probability and detection time.

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Section: Odds Ratio Valuesmentioning

confidence: 84%

“…In [17] a novel timebased anomaly detection system is presented, where that system leverages an autoencoder. By using concepts of machine learning supervised classification, a tensor based framework is proposed for DDoS attack detection [18]. Also many signal processing techniques have been used to detect anomalies in network traffic [19].…”

Section: Introductionmentioning

confidence: 99%

Detection of Distributed Denial of Service Flooding Attack Using Odds Ratio

Nashat¹,

Hussain²,

Jiang³

2021

J-NaNA

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“…Network-based attacks represent threats that originate from and are regulated by devices other than those under attack. A DDoS attack is a network-based attack example in which systems of intrusion prevention and firewalls are countermeasures to this attack type (Maranhão et al, 2021). A host-based IDS monitors and analyses the computing system internals.…”

Section: Methodsmentioning

confidence: 99%

Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction

Dheyab¹,

Abdulameer²,

Mostafa³

2022

AIP

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Distributed denial of service (DDoS) attacks are one of the most common global challenges faced by service providers on the web. It leads to network disturbances, interruption of communication and significant damage to services. Researchers seek to develop intelligent algorithms to detect and prevent DDoS attacks. The present study proposes an efficient DDoS attack detection model. This model relies mainly on dimensionality reduction and machine learning algorithms. The principal component analysis (PCA) and the linear discriminant analysis (LDA) techniques perform the dimensionality reduction in individual and hybrid modes to process and improve the data. Subsequently, DDoS attack detection is performed based on random forest (RF) and decision tree (DT) algorithms. The model is implemented and tested on the CICDDoS2019 dataset using different data dimensionality reduction test scenarios. The results show that using dimensionality reduction techniques along with the ML algorithms with a dataset containing high-dimensional data significantly improves the classification results. The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40.

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“…Only five attacks were considered in this research. Binary classification was done in [20,26] which produced higher accuracy levels than multiclass classification [29]. Furthermore, AdaBoost and deep learning techniques were always slower than LGBM and XGBoost [30].…”

Section: Figure 8 Classification Report With Udp-lagmentioning

confidence: 99%

Boosting Algorithms to Identify Distributed Denial-of-Service Attacks

Kumar

Garg

et al. 2022

J. Phys.: Conf. Ser.

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In the current pandemic situation, much work became automated using Internet of Things (IoT) devices. The security of IoT devices is a major issue because they can easily be hacked by third parties. Attackers cause interruptions in vital ongoing operations through these hacked devices. Thus, the demand for an efficient attack identification system has increased in the last few years. The present research aims to identify modern distributed denial-of-service (DDoS) attacks. To provide a solution to the problem of DDoS attacks, an openly available dataset (CICDDoS 2019) has recently been introduced and implemented. The attacks currently occurring in the dataset were identified using two machine learning methods, i.e. the light gradient boosting method (LGBM) and extreme gradient boosting (XGBoost). These methods have been selected because of their superior prediction ability in high volumes of data in less time than other methods require. The accuracy achieved by LGBM and XGBoost were 94.88% and 94.89% in 30 and 229 seconds(s), respectively.

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Tensor based framework for Distributed Denial of Service attack detection

Cited by 18 publications

References 28 publications

Detection of Distributed Denial of Service Flooding Attack Using Odds Ratio

Detection of Distributed Denial of Service Flooding Attack Using Odds Ratio

Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction

Boosting Algorithms to Identify Distributed Denial-of-Service Attacks

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